脑白质疏松症弥散张量成像的探讨

脑白质疏松症发病率逐渐增高,其相关的神经功能障碍严重影响生活质量,因此早期发现LA患者存在的隐匿性损伤对早期治疗及预防有重要临床意义。LA的病理生理学特点为血管内皮细胞的受损引起血管通透性的改变,从而使周围组织的弥散程度发生改变,细胞外水分子运动对信号的改变起主导作用。DTI是目前检测脑白质唯一的无创性方法,可从量和方向上反映成像的体素内水分子扩散的变化,可以测量组织中扩散的各向异性。DTI较传统的MR能更好的反应神经系统白质的超微结构的改变,为影像学与其病理生理的相关性研究提供新的方法。目前对脑白质疏松的研究主要集中在与神经功能相关区域DTI量化指标的改变,因此本文对脑白质疏松的DTI技术的应用及发展趋势进行综述。     

Simultaneous Analysis and Quality Assurance for Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) enables non-invasive, cyto-architectural mapping of in vivo tissue microarchitecture through voxel-wise mathematical modeling of multiple magnetic resonance imaging (MRI) acquisitions, each differently sensitized to water diffusion. DTI computations are fundamentally estimation processes and are sensitive to noise and artifacts. Despite widespread adoption in the neuroimaging community, maintaining consistent DTI data quality remains challenging given the propensity for patient motion, artifacts associated with fast imaging techniques, and the possibility of hardware changes/failures. Furthermore, the quantity of data acquired per voxel, the non-linear estimation process, and numerous potential use cases complicate traditional visual data inspection approaches. Currently, quality inspection of DTI data has relied on visual inspection and individual processing in DTI analysis software programs (e.g. DTIPrep, DTI-studio). However, recent advances in applied statistical methods have yielded several different metrics to assess noise level, artifact propensity, quality of tensor fit, variance of estimated measures, and bias in estimated measures. To date, these metrics have been largely studied in isolation. Herein, we select complementary metrics for integration into an automatic DTI analysis and quality assurance pipeline. The pipeline completes in 24 hours, stores statistical outputs, and produces a graphical summary quality analysis (QA) report. We assess the utility of this streamlined approach for empirical quality assessment on 608 DTI datasets from pediatric neuroimaging studies. The efficiency and accuracy of quality analysis using the proposed pipeline is compared with quality analysis based on visual inspection. The unified pipeline is found to save a statistically significant amount of time (over 70%) while improving the consistency of QA between a DTI expert and a pool of research associates. Projection of QA metrics to a low dimensional manifold reveal qualitative, but clear, QA-study associations and suggest that automated outlier/anomaly detection would be feasible.     

Diffusion Tensor Magnetic Resonance Imaging of the Pancreas

Purpose

To develop a diffusion-tensor-imaging (DTI) protocol that is sensitive to the complex diffusion and perfusion properties of the healthy and malignant pancreas tissues.

Materials and Methods

Twenty-eight healthy volunteers and nine patients with pancreatic-ductal-adenocacinoma (PDAC), were scanned at 3T with T2-weighted and DTI sequences. Healthy volunteers were also scanned with multi-b diffusion-weighted-imaging (DWI), whereas a standard clinical protocol complemented the PDAC patients’ scans. Image processing at pixel resolution yielded parametric maps of three directional diffusion coefficients λ1, λ2, λ3, apparent diffusion coefficient (ADC), and fractional anisotropy (FA), as well as a λ1-vector map, and a main diffusion-direction map.

Results

DTI measurements of healthy pancreatic tissue at b-values 0,500 s/mm²yielded: λ1 = (2.65±0.35)×10⁻³, λ2 = (1.87±0.22)×10⁻³, λ3 = (1.20±0.18)×10⁻³, ADC = (1.91±0.22)×10⁻³ (all in mm²/s units) and FA = 0.38±0.06. Using b-values of 100,500 s/mm² led to a significant reduction in λ1, λ2, λ3 and ADC (p<.0001) and a significant increase (p<0.0001) in FA. The reduction in the diffusion coefficients suggested a contribution of a fast intra-voxel-incoherent-motion (IVIM) component at b≤100 s/mm², which was confirmed by the multi-b DWI results. In PDACs, λ1, λ2, λ3 and ADC in both 0,500 s/mm² and 100,500 s/mm² b-values sets, as well as the reduction in these diffusion coefficients between the two sets, were significantly lower in comparison to the distal normal pancreatic tissue, suggesting higher cellularity and diminution of the fast-IVIM component in the cancer tissue.

Conclusion

DTI using two reference b-values 0 and 100 s/mm² enabled characterization of the water diffusion and anisotropy of the healthy pancreas, taking into account a contribution of IVIM. The reduction in the diffusion coefficients of PDAC, as compared to normal pancreatic tissue, and the smaller change in these coefficients in PDAC when the reference b-value was modified from 0 to 100 s/mm², helped identifying the presence of malignancy.     

Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging

Newly emerging theories suggest that the brain does not function as a cohesive unit in autism, and this discordance is reflected in the behavioral symptoms displayed by individuals with autism. While structural neuroimaging findings have provided some insights into brain abnormalities in autism, the consistency of such findings is questionable. Functional neuroimaging, on the other hand, has been more fruitful in this regard because autism is a disorder of dynamic processing and allows examination of communication between cortical networks, which appears to be where the underlying problem occurs in autism. Functional connectivity is defined as the temporal correlation of spatially separate neurological events1. Findings from a number of recent fMRI studies have supported the idea that there is weaker coordination between different parts of the brain that should be working together to accomplish complex social or language problems^2,3,4,5,6. One of the mysteries of autism is the coexistence of deficits in several domains along with relatively intact, sometimes enhanced, abilities. Such complex manifestation of autism calls for a global and comprehensive examination of the disorder at the neural level. A compelling recent account of the brain functioning in autism, the cortical underconnectivity theory,^2,7 provides an integrating framework for the neurobiological bases of autism. The cortical underconnectivity theory of autism suggests that any language, social, or psychological function that is dependent on the integration of multiple brain regions is susceptible to disruption as the processing demand increases. In autism, the underfunctioning of integrative circuitry in the brain may cause widespread underconnectivity. In other words, people with autism may interpret information in a piecemeal fashion at the expense of the whole. Since cortical underconnectivity among brain regions, especially the frontal cortex and more posterior areas ^3,6, has now been relatively well established, we can begin to further understand brain connectivity as a critical component of autism symptomatology.A logical next step in this direction is to examine the anatomical connections that may mediate the functional connections mentioned above. Diffusion Tensor Imaging (DTI) is a relatively novel neuroimaging technique that helps probe the diffusion of water in the brain to infer the integrity of white matter fibers. In this technique, water diffusion in the brain is examined in several directions using diffusion gradients. While functional connectivity provides information about the synchronization of brain activation across different brain areas during a task or during rest, DTI helps in understanding the underlying axonal organization which may facilitate the cross-talk among brain areas. This paper will describe these techniques as valuable tools in understanding the brain in autism and the challenges involved in this line of research. Download video file.^{(73M, mov)}     

fMRI与DTI在神经科学研究中的联合应用

功能磁共振成像(fMRI)和扩散张量成像(DTI)是近年来磁共振成像领域出现的两种新的成像技术,它们各具特色。功能磁共振成像能对人脑相关任务激活区进行准确的功能定位并提供相关皮层区域的磁共振信号改变特征信息,但时于脑白质相关改变则不能提供任何信息;扩散张量成像则是目前能够在体呈现人脑解剖连接的唯一手段,采用它能对人脑组织,包括灰质和白质的扩散特性进行定量研究,并且能够形象显示人脑生理或病理状态下的纤维束形态、走行等,但扩散张量成像不能提供皮层功能情况信息。功能磁共振成像和扩散张量成像技术具有很强的互补性,二者联合在神经科学研究中具有广阔的应用前景。目前也正成为神经科学研究领域的热点之一。本文从功能磁共振成像和扩散张量成像的原理、特点,二者结合应用的具体方法以及目前二者在神经科学各基础及临床学科结合应用的研究进展进行了综述。     

Image Corruption Detection in Diffusion Tensor Imaging for Post-Processing and Real-Time Monitoring

Due to the high sensitivity of diffusion tensor imaging (DTI) to physiological motion, clinical DTI scans often suffer a significant amount of artifacts. Tensor-fitting-based, post-processing outlier rejection is often used to reduce the influence of motion artifacts. Although it is an effective approach, when there are multiple corrupted data, this method may no longer correctly identify and reject the corrupted data. In this paper, we introduce a new criterion called “corrected Inter-Slice Intensity Discontinuity” (cISID) to detect motion-induced artifacts. We compared the performance of algorithms using cISID and other existing methods with regard to artifact detection. The experimental results show that the integration of cISID into fitting-based methods significantly improves the retrospective detection performance at post-processing analysis. The performance of the cISID criterion, if used alone, was inferior to the fitting-based methods, but cISID could effectively identify severely corrupted images with a rapid calculation time. In the second part of this paper, an outlier rejection scheme was implemented on a scanner for real-time monitoring of image quality and reacquisition of the corrupted data. The real-time monitoring, based on cISID and followed by post-processing, fitting-based outlier rejection, could provide a robust environment for routine DTI studies.     

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Diffusion tensor imaging (DTI) techniques provide information on the microstructural processes of the cerebral white matter (WM) in vivo. The present applications are designed to investigate differences of WM involvement patterns in different brain diseases, especially neurodegenerative disorders, by use of different DTI analyses in comparison with matched controls. DTI data analysis is performed in a variate fashion, i.e. voxelwise comparison of regional diffusion direction-based metrics such as fractional anisotropy (FA), together with fiber tracking (FT) accompanied by tractwise fractional anisotropy statistics (TFAS) at the group level in order to identify differences in FA along WM structures, aiming at the definition of regional patterns of WM alterations at the group level. Transformation into a stereotaxic standard space is a prerequisite for group studies and requires thorough data processing to preserve directional inter-dependencies. The present applications show optimized technical approaches for this preservation of quantitative and directional information during spatial normalization in data analyses at the group level. On this basis, FT techniques can be applied to group averaged data in order to quantify metrics information as defined by FT. Additionally, application of DTI methods, i.e. differences in FA-maps after stereotaxic alignment, in a longitudinal analysis at an individual subject basis reveal information about the progression of neurological disorders. Further quality improvement of DTI based results can be obtained during preprocessing by application of a controlled elimination of gradient directions with high noise levels.In summary, DTI is used to define a distinct WM pathoanatomy of different brain diseases by the combination of whole brain-based and tract-based DTI analysis.     

Inter Subject Variability and Reproducibility of Diffusion Tensor Imaging within and between Different Imaging Sessions

The aim of these studies was to provide reference data on intersubject variability and reproducibility of diffusion tensor imaging. Healthy volunteers underwent imaging on two occasions using the same 3T Siemens Verio magnetic resonance scanner. At each session two identical diffusion tensor sequences were obtained along with standard structural imaging. Fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity maps were created and regions of interest applied in normalised space. The baseline data from all 26 volunteers were used to calculate the intersubject variability, while within session and between session reproducibility were calculated from all the available data. The reproducibility of measurements were used to calculate the overall and within session 95% prediction interval for zero change. The within and between session reproducibility data were lower than the values for intersubject variability, and were different across the brain. The regional mean (range) coefficient of variation figures for within session reproducibility were 2.1 (0.9–5.5%), 1.2 (0.4–3.9%), 1.2 (0.4–3.8%) and 1.8 (0.4–4.3%) for fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity, and were lower than between session reproducibility measurements (2.4 (1.1–5.9%), 1.9 (0.7–5.7%), 1.7 (0.7–4.7%) and 2.4 (0.9–5.8%); p<0.001). The calculated overall and within session 95% prediction intervals for zero change were similar. This study provides additional reference data concerning intersubject variability and reproducibility of diffusion tensor imaging conducted within the same imaging session and different imaging sessions. These data can be utilised in interventional studies to quantify change within a single imaging session, or to assess the significance of change in longitudinal studies of brain injury and disease.     

Examination of Cognitive Fatigue in Multiple Sclerosis using Functional Magnetic Resonance Imaging and Diffusion Tensor Imaging

The present study investigated the neural correlates of cognitive fatigue in Multiple Sclerosis (MS), looking specifically at the relationship between self-reported fatigue and objective measures of cognitive fatigue. In Experiment 1, functional magnetic resonance imaging (fMRI) was used to examine where in the brain BOLD activity covaried with “state” fatigue, assessed during performance of a task designed to induce cognitive fatigue while in the scanner. In Experiment 2, diffusion tensor imaging (DTI) was used to examine where in the brain white matter damage correlated with increased “trait” fatigue in individuals with MS, assessed by the Fatigue Severity Scale (FSS) completed outside the scanning session. During the cognitively fatiguing task, the MS group had increased brain activity associated with fatigue in the caudate as compared with HCs. DTI findings revealed that reduced fractional anisotropy in the anterior internal capsule was associated with increased self-reported fatigue on the FSS. Results are discussed in terms of identifying a “fatigue-network” in MS.     

Diurnal Microstructural Variations in Healthy Adult Brain Revealed by Diffusion Tensor Imaging

Biorhythm is a fundamental property of human physiology. Changes in the extracellular space induced by cell swelling in response to the neural activity enable the in vivo characterization of cerebral microstructure by measuring the water diffusivity using diffusion tensor imaging (DTI). To study the diurnal microstructural alterations of human brain, fifteen right-handed healthy adult subjects were recruited for DTI studies in two repeated sessions (8∶30 AM and 8∶30 PM) within a 24-hour interval. Fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial (λ_//) and radial diffusivity (λ_⊥) were compared pixel by pixel between the sessions for each subject. Significant increased morning measurements in FA, ADC, λ_// and λ_⊥ were seen in a wide range of brain areas involving frontal, parietal, temporal and occipital lobes. Prominent evening dominant λ_⊥ (18.58%) was detected in the right inferior temporal and ventral fusiform gyri. AM-PM variation of λ_⊥ was substantially left side hemisphere dominant (p<0.05), while no hemispheric preference was observed for the same analysis for ADC (p = 0.77), λ_// (p = 0.08) or FA (p = 0.25). The percentage change of ADC, λ_//, λ_⊥, and FA were 1.59%, 2.15%, 1.20% and 2.84%, respectively, for brain areas without diurnal diffusivity contrast. Microstructural variations may function as the substrates of the phasic neural activities in correspondence to the environment adaptation in a light-dark cycle. This research provided a baseline for researches in neuroscience, sleep medicine, psychological and psychiatric disorders, and necessitates that diurnal effect should be taken into account in following up studies using diffusion tensor quantities.     

Diffusion Tensor Magnetic Resonance Imaging of Trigeminal Nerves in Relapsing Herpetic Keratouveitis

Background

Corneal hypoesthesia is the landmark of HSV and VZV keratitis and can lead to neurotrophic keratitis. Diffusion tensor imaging (DTI) is a new magnetic resonance imaging (MRI) derived technique, which offers possibilities to study axonal architecture. We aimed at assessing the potential impact of recurrent HSV or VZV-related keratitis on the axonal architecture of trigeminal nerves using DTI.

Design

Prospective non-interventional study.

Participants

Twelve patients and 24 controls.

Methods

DTI using MRI of the trigeminal fibers and corneal esthesiometry using the Cochet-Bonnet esthesiometer were acquired for patients affected by unilateral and recurrent HSV or VZV-related keratitis (3 months after the last corneal inflammatory event), and control subjects with no history of ocular or neuronal disease affecting the trigeminal pathways.

Main Outcome Measures

Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were compared between the 2 eyes of both patients and controls, and correlated with corneal esthesiometry.

Results

FA was lower in the trigeminal fibers ipsilateral to the affected eye compared to the non-affected side (0.39±0.02 versus 0.46±0.04, P=0.03). This difference was more important than the intra-individual variability observed in controls. Concomitantly, the asymmetry in ADC results was significantly correlated with the loss of corneal sensitivity in the affected eye.

Conclusions

Corneal hypoesthesia related to HSV and VZV keratitis is associated with persistent modifications in the architecture and functionality of the trigeminal fibers. These results add further explanation to the pathogenesis of HSV and VZV-induced neurotrophic keratitis, which may occur despite an apparent quiescence of the disease.     

Tractography of the Brainstem in Major Depressive Disorder Using Diffusion Tensor Imaging

Background

The brainstem is the main region that innervates neurotransmitter release to the Hypothalamic-Pituitary Adrenal (HPA) axis and fronto-limbic circuits, two key brain circuits found to be dysfunctional in Major Depressive Disorder (MDD). However, the brainstem’s role in MDD has only been evaluated in limited reports. Using Diffusion Tensor Imaging (DTI), we investigated whether major brainstem white matter tracts that relate to these two circuits differ in MDD patients compared to healthy controls.

Methods

MDD patients (n = 95) and age- and gender-matched controls (n = 34) were assessed using probabilistic tractography of DTI to delineate three distinct brainstem tracts: the nigrostriatal tract (connecting brainstem to striatum), solitary tract (connecting brainstem to amygdala) and corticospinal tract (connecting brainstem to precentral cortex). Fractional anisotropy (FA) was used to measure the white matter integrity of these tracts, and measures were compared between MDD and control participants.

Results

MDD participants were characterized by a significant and specific decrease in white matter integrity of the right solitary tract (p<0.009 using independent t-test), which is a “bottom up” afferent pathway that connects the brainstem to the amygdala. This decrease was not related to symptom severity.

Conclusions

The results provide new evidence to suggest that structural connectivity between the brainstem and the amygdala is altered in MDD. These results are interesting in light of predominant theories regarding amygdala-mediated emotional reactivity observed in functional imaging studies of MDD. The characterization of altered white matter integrity in the solitary tract in MDD supports the possibility of dysfunctional brainstem-amygdala connectivity impacting vulnerable circuits in MDD.     

Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model

Purpose

Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-infiltrated regions and peritumoral edema. Here, we describe the supertoroidal model, which incorporates an increase in surface genus and a continuum of toroidal shapes to improve upon the characterization of Glioblastoma multiforme (GBM).

Materials and Methods

DTI brain datasets of 18 individuals with GBM and 18 normal subjects were acquired using a 3T scanner. A supertoroidal model of the diffusion tensor and two new diffusion tensor invariants, one to evaluate diffusivity, the toroidal volume (TV), and one to evaluate anisotropy, the toroidal curvature (TC), were applied and evaluated in the characterization of GBM brain tumors. TV and TC were compared with the mean diffusivity (MD) and fractional anisotropy (FA) indices inside the tumor, surrounding edema, as well as contralateral to the lesions, in the white matter (WM) and gray matter (GM).

Results

The supertoroidal model enhanced the borders between tumors and surrounding structures, refined the boundaries between WM and GM, and revealed the heterogeneity inherent to tumor-infiltrated tissue. Both MD and TV demonstrated high intensities in the tumor, with lower values in the surrounding edema, which in turn were higher than those of unaffected brain parenchyma. Both TC and FA were effective in revealing the structural degradation of WM tracts.

Conclusions

Our findings indicate that the supertoroidal model enables effective tensor visualization as well as quantitative scalar maps that improve the understanding of the underlying tissue structure properties. Hence, this approach has the potential to enhance diagnosis, preoperative planning, and intraoperative image guidance during surgical management of brain lesions.     

Delineation of Early and Later Adult Onset Depression by Diffusion Tensor Imaging

Background

Due to a lack of evidence, there is no consistent age of onset to define early onset (EO) versus later onset (LO) major depressive disorder (MDD). Fractional anisotropy (FA), derived from diffusion tensor imaging (DTI), has been widely used to study neuropsychiatric disorders by providing information about the brain circuitry, abnormalities of which might facilitate the delineation of EO versus LO MDD.

Method

In this study, 61 pairs of untreated, non-elderly, first-episode MDD patients and healthy controls (HCs) aged 18–45 years old received DTI scans. The voxel-based analysis method (VBM), classification analysis, using the Statistical Package for the Social Sciences (SPSS), and regression analyses were used to determine abnormal FA clusters and their correlations with age of onset and clinical symptoms.

Results

Classification analysis suggested in the best model that there were two subgroups of MDD patients, delineated by an age of onset of 30 years old, by which MDD patients could be divided into EO (18–29 years old) and LO (30–45 years old) groups. LO MDD was characterized by decreased FA, especially in the white matter (WM) of the fronto-occipital fasciculus and posterior limb of internal capsule, with a negative correlation with the severity of depressive symptoms; in marked contrast, EO MDD showed increased FA, especially in the WM of the corpus callosum, corticospinal midbrain and inferior fronto-occipital fasciculus, while FA of the WM near the midbrain had a positive correlation with the severity of depressive symptoms.

Conclusion

Specific abnormalities of the brain circuitry in EO vs. LO MDD were delineated by an age of onset of 30 years old, as demonstrated by distinct abnormal FA clusters with opposite correlations with clinical symptoms. This DTI study supported the evidence of an exact age for the delineation of MDD, which could have broad multidisciplinary importance.

Trial Registration

ClinicalTrials.gov {"type":"clinical-trial","attrs":{"text":"NCT00703742","term_id":"NCT00703742"}}NCT00703742     

Kinematic and Diffusion Tensor Imaging Definition of Familial Marcus Gunn Jaw-Winking Synkinesis

Background

Marcus Gunn jaw-winking synkinesis (MGJWS) is characterized by eyelid ptosis, which disappears during jaw movement. Familial MGJWS is an extremely rare condition. Some authors suggested that MGJWS is due to neural misdirection in the brainstem whereas others suggested that aberrant reinnervation or ephapse may be responsible for synkinetic activity. Pathogenesis of this condition is therefore still unclear.

Methodology/Principal Findings

To investigate pathogenetic mechanism in familial MGJWS we performed neurophysiological (EMG, Blink Reflex, Recovery cycle of the R2 component of the blink reflex, Masseter inhibitory reflex, BAEPS and kinematic analysis) and neuroradiological (MRI, Diffusion Tensor Imaging) investigations in a member of a multigenerational family with autosomal dominant Marcus Gunn jaw-winking synkinesis (MGJWS). Kinematic analysis of eyelid and jaw movements disclosed a similar onset and offset of the eyelid and jaw in both the opening and closing phases. The excitability of brainstem circuits, as assessed by the blink reflex recovery cycle and recovery index, was normal. Diffusion Tensor Imaging revealed reduced fractional anisotropy within the midbrain tegmentum.

Conclusions/Significance

Kinematic and MRI findings point to a brainstem structural abnormality in our familial MGJWS patient thus supporting the hypothesis of a neural misdirection of trigeminal motor axons to the elevator palpebralis muscle.     

Renal Water Molecular Diffusion Characteristics in Healthy Native Kidneys: Assessment with Diffusion Tensor MR Imaging

Background

To explore the characteristics of diffusion tensor imaging (DTI) and magnetic resonance (MR) imaging in healthy native kidneys.

Methods

Seventy-three patients without chronic kidney disease underwent DTI-MRI with spin echo-echo planar (SE-EPI) sequences accompanied by an array spatial sensitivity encoding technique (ASSET). Cortical and medullary mean, axial and radial diffusivity (MD, AD and RD), fractional anisotropy (FA) and primary, secondary and tertiary eigenvalues (λ₁, λ₂, λ₃) were analysed in both kidneys and in different genders.

Results

Cortical MD, λ₂, λ₃, and RD values were higher than corresponding medullary values. The cortical FA value was lower than the medullary FA value. Medullary λ₁ and RD values in the left kidney were lower than in the right kidney. Medullary λ₂, and λ₃ values in women were higher than those in men. Medullary FA values in women were lower than those in men. Medullary FA (r = 0.351, P = 0.002) and λ₁ (r = 0.277, P = 0.018) positively correlated with eGFR. Medullary FA (r = −0.25, P = 0.033) negatively correlated with age.

Conclusions

Renal water molecular diffusion differences exist in human kidneys and genders. Age and eGFR correlate with medullary FA and primary eigenvalue.     

Reproducibility of the Structural Brain Connectome Derived from Diffusion Tensor Imaging

Rationale

Disruptions of brain anatomical connectivity are believed to play a central role in several neurological and psychiatric illnesses. The structural brain connectome is typically derived from diffusion tensor imaging (DTI), which may be influenced by methodological factors related to signal processing, MRI scanners and biophysical properties of neuroanatomical regions. In this study, we evaluated how these variables affect the reproducibility of the structural connectome.

Methods

Twenty healthy adults underwent 3 MRI scanning sessions (twice in the same MRI scanner and a third time in a different scanner unit) within a short period of time. The scanning sessions included similar T1 weighted and DTI sequences. Deterministic or probabilistic tractography was performed to assess link weight based on the number of fibers connecting gray matter regions of interest (ROI). Link weight and graph theory network measures were calculated and reproducibility was assessed through intra-class correlation coefficients, assuming each scanning session as a rater.

Results

Connectome reproducibility was higher with data from the same scanner. The probabilistic approach yielded larger reproducibility, while the individual variation in the number of tracked fibers from deterministic tractography was negatively associated with reproducibility. Links connecting larger and anatomically closer ROIs demonstrated higher reproducibility. In general, graph theory measures demonstrated high reproducibility across scanning sessions.

Discussion

Anatomical factors and tractography approaches can influence the reproducibility of the structural connectome and should be factored in the interpretation of future studies. Our results demonstrate that connectome mapping is a largely reproducible technique, particularly as it relates to the geometry of network architecture measured by graph theory methods.     

Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain

In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8–0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images – an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure.     

Neuropsychological Outcome and Diffusion Tensor Imaging in Complicated versus Uncomplicated Mild Traumatic Brain Injury

This study examined whether intracranial neuroimaging abnormalities in those with mild traumatic brain injury (MTBI) (i.e., “complicated” MTBIs) are associated with worse subacute outcomes as measured by cognitive testing, symptom ratings, and/or diffusion tensor imaging (DTI). We hypothesized that (i) as a group, participants with complicated MTBIs would report greater symptoms and have worse neurocognitive outcomes than those with uncomplicated MTBI, and (ii) as a group, participants with complicated MTBIs would show more Diffusion Tensor Imaging (DTI) abnormalities. Participants were 62 adults with MTBIs (31 complicated and 31 uncomplicated) who completed neurocognitive testing, symptom ratings, and DTI on a 3T MRI scanner approximately 6-8 weeks post injury. There were no statistically significant differences between groups on symptom ratings or on a broad range of neuropsychological tests. When comparing the groups using tract-based spatial statistics for DTI, no significant difference was found for axial diffusivity or mean diffusivity. However, several brain regions demonstrated increased radial diffusivity (purported to measure myelin integrity), and decreased fractional anisotropy in the complicated group compared with the uncomplicated group. Finally, when we extended the DTI analysis, using a multivariate atlas based approach, to 32 orthopedic trauma controls (TC), the findings did not reveal significantly more areas of abnormal DTI signal in the complicated vs. uncomplicated groups, although both MTBI groups had a greater number of areas with increased radial diffusivity compared with the trauma controls. This study illustrates that macrostructural neuroimaging changes following MTBI are associated with measurable changes in DTI signal. Of note, however, the division of MTBI into complicated and uncomplicated subtypes did not predict worse clinical outcome at 6-8 weeks post injury.     

扩散张量成像对颈椎病脊髓早期损伤的研究

目的:探讨磁共振(MR)扩散张量成像(DTI)作为定量分析方法,对脊髓型颈椎病(CSM)脊髓早期损伤诊断的应用价值.方法:选择45例经临床及影像诊断为脊髓型颈椎病患者,颈椎常规MRI检查显示脊髓内无异常信号,使用单次激发自旋回波平面(SE-EPI)序列,进行DTI扫描.测量压迫部位脊髓的ADC值及FA值作为病例组,选择病变上或下方两个节段以上未受压正常脊髓作为正常对照组,测量其ADC值及FA值.分析病例组与对照组间ADC及FA值差别,计算ADC值及FA值诊断脊髓损伤的敏感性.结果:所有脊髓型颈椎病患者经DTI检查均可得到ADC图及FA图,经图像后处理,脊髓显示清晰,图像无变形及伪影.3例脊髓型颈椎病患者ADC值降低,42例脊髓型颈椎病患者ADC值增高,平均ADC值为(1.388± 0.149)x 10-3 mm2/s.44名脊髓型颈椎病患者FA值降低,1名脊髓型颈椎病患者FA值增高,平均FA值为0.476±0.085,受压处脊髓平均ADC值升高,平均FA值下降,与正常值比较差别有统计学意义.ADC值诊断的敏感性为93.33％,FA值诊断的敏感性为97.78％.结论:DTI与常规MR比较,能早期而准确地诊断脊髓型颈椎病脊髓早期损伤.